{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "czech-musical",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "rental-possible",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensorflow version:2.4.0\n"
     ]
    }
   ],
   "source": [
    "print(\"tensorflow version:{}\".format(tf.__version__))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "forty-worker",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "shaped-component",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv(\"./222.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "aboriginal-pulse",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>kfxs</th>\n",
       "      <th>mklz</th>\n",
       "      <th>csd</th>\n",
       "      <th>pjcd</th>\n",
       "      <th>zqd</th>\n",
       "      <th>dqzs</th>\n",
       "      <th>dlbqd</th>\n",
       "      <th>fsl</th>\n",
       "      <th>hd</th>\n",
       "      <th>mj</th>\n",
       "      <th>scdlqd</th>\n",
       "      <th>sjhs</th>\n",
       "      <th>xscvm</th>\n",
       "      <th>xscvb</th>\n",
       "      <th>xi40</th>\n",
       "      <th>xi50</th>\n",
       "      <th>cu50</th>\n",
       "      <th>xsmj140</th>\n",
       "      <th>xsmj200</th>\n",
       "      <th>xshz</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>126.43</td>\n",
       "      <td>4.84</td>\n",
       "      <td>0.90</td>\n",
       "      <td>29.04</td>\n",
       "      <td>81.89</td>\n",
       "      <td>7.14</td>\n",
       "      <td>28.28</td>\n",
       "      <td>77.14</td>\n",
       "      <td>7.44</td>\n",
       "      <td>224.10</td>\n",
       "      <td>14.6</td>\n",
       "      <td>22.7</td>\n",
       "      <td>10.39</td>\n",
       "      <td>3.2</td>\n",
       "      <td>8.8</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>5.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>126.43</td>\n",
       "      <td>4.84</td>\n",
       "      <td>0.90</td>\n",
       "      <td>29.04</td>\n",
       "      <td>81.89</td>\n",
       "      <td>7.14</td>\n",
       "      <td>28.28</td>\n",
       "      <td>77.14</td>\n",
       "      <td>7.44</td>\n",
       "      <td>224.10</td>\n",
       "      <td>15.0</td>\n",
       "      <td>22.3</td>\n",
       "      <td>10.54</td>\n",
       "      <td>3.2</td>\n",
       "      <td>8.8</td>\n",
       "      <td>0.3</td>\n",
       "      <td>1.8</td>\n",
       "      <td>42.3</td>\n",
       "      <td>6.8</td>\n",
       "      <td>5.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>125.64</td>\n",
       "      <td>4.64</td>\n",
       "      <td>0.89</td>\n",
       "      <td>29.63</td>\n",
       "      <td>81.60</td>\n",
       "      <td>7.13</td>\n",
       "      <td>27.28</td>\n",
       "      <td>77.85</td>\n",
       "      <td>7.34</td>\n",
       "      <td>253.00</td>\n",
       "      <td>14.8</td>\n",
       "      <td>20.5</td>\n",
       "      <td>10.34</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>25.8</td>\n",
       "      <td>6.3</td>\n",
       "      <td>5.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>124.21</td>\n",
       "      <td>4.67</td>\n",
       "      <td>0.89</td>\n",
       "      <td>29.60</td>\n",
       "      <td>81.36</td>\n",
       "      <td>7.17</td>\n",
       "      <td>27.31</td>\n",
       "      <td>77.64</td>\n",
       "      <td>7.32</td>\n",
       "      <td>251.14</td>\n",
       "      <td>14.8</td>\n",
       "      <td>20.5</td>\n",
       "      <td>10.34</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>25.8</td>\n",
       "      <td>6.3</td>\n",
       "      <td>5.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>128.10</td>\n",
       "      <td>4.58</td>\n",
       "      <td>0.89</td>\n",
       "      <td>30.12</td>\n",
       "      <td>81.68</td>\n",
       "      <td>7.24</td>\n",
       "      <td>27.54</td>\n",
       "      <td>77.57</td>\n",
       "      <td>7.31</td>\n",
       "      <td>219.00</td>\n",
       "      <td>15.7</td>\n",
       "      <td>20.8</td>\n",
       "      <td>10.54</td>\n",
       "      <td>1.4</td>\n",
       "      <td>4.8</td>\n",
       "      <td>0.3</td>\n",
       "      <td>5.3</td>\n",
       "      <td>64.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>5.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>920</th>\n",
       "      <td>148.70</td>\n",
       "      <td>4.33</td>\n",
       "      <td>0.93</td>\n",
       "      <td>29.16</td>\n",
       "      <td>82.91</td>\n",
       "      <td>9.40</td>\n",
       "      <td>32.58</td>\n",
       "      <td>76.75</td>\n",
       "      <td>8.48</td>\n",
       "      <td>247.70</td>\n",
       "      <td>12.9</td>\n",
       "      <td>18.5</td>\n",
       "      <td>12.34</td>\n",
       "      <td>2.0</td>\n",
       "      <td>52.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>24.5</td>\n",
       "      <td>244.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>5.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>921</th>\n",
       "      <td>149.60</td>\n",
       "      <td>4.32</td>\n",
       "      <td>0.93</td>\n",
       "      <td>29.20</td>\n",
       "      <td>82.92</td>\n",
       "      <td>9.39</td>\n",
       "      <td>32.81</td>\n",
       "      <td>76.76</td>\n",
       "      <td>8.43</td>\n",
       "      <td>247.13</td>\n",
       "      <td>12.9</td>\n",
       "      <td>18.5</td>\n",
       "      <td>12.34</td>\n",
       "      <td>2.0</td>\n",
       "      <td>52.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>24.5</td>\n",
       "      <td>244.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>5.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>922</th>\n",
       "      <td>150.03</td>\n",
       "      <td>4.31</td>\n",
       "      <td>0.93</td>\n",
       "      <td>29.23</td>\n",
       "      <td>82.95</td>\n",
       "      <td>9.36</td>\n",
       "      <td>32.86</td>\n",
       "      <td>76.85</td>\n",
       "      <td>8.43</td>\n",
       "      <td>245.10</td>\n",
       "      <td>12.9</td>\n",
       "      <td>18.5</td>\n",
       "      <td>12.34</td>\n",
       "      <td>2.0</td>\n",
       "      <td>52.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>24.5</td>\n",
       "      <td>244.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>5.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>923</th>\n",
       "      <td>150.03</td>\n",
       "      <td>4.31</td>\n",
       "      <td>0.93</td>\n",
       "      <td>29.23</td>\n",
       "      <td>82.95</td>\n",
       "      <td>9.36</td>\n",
       "      <td>32.86</td>\n",
       "      <td>76.85</td>\n",
       "      <td>8.43</td>\n",
       "      <td>245.10</td>\n",
       "      <td>12.9</td>\n",
       "      <td>18.5</td>\n",
       "      <td>12.34</td>\n",
       "      <td>2.0</td>\n",
       "      <td>52.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>24.5</td>\n",
       "      <td>244.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>5.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>924</th>\n",
       "      <td>150.93</td>\n",
       "      <td>4.30</td>\n",
       "      <td>0.93</td>\n",
       "      <td>29.27</td>\n",
       "      <td>82.96</td>\n",
       "      <td>9.34</td>\n",
       "      <td>33.09</td>\n",
       "      <td>76.86</td>\n",
       "      <td>8.38</td>\n",
       "      <td>244.53</td>\n",
       "      <td>12.9</td>\n",
       "      <td>18.5</td>\n",
       "      <td>12.34</td>\n",
       "      <td>2.0</td>\n",
       "      <td>52.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>24.5</td>\n",
       "      <td>244.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>5.42</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>925 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       kfxs  mklz   csd   pjcd    zqd  dqzs  dlbqd    fsl    hd      mj  \\\n",
       "0    126.43  4.84  0.90  29.04  81.89  7.14  28.28  77.14  7.44  224.10   \n",
       "1    126.43  4.84  0.90  29.04  81.89  7.14  28.28  77.14  7.44  224.10   \n",
       "2    125.64  4.64  0.89  29.63  81.60  7.13  27.28  77.85  7.34  253.00   \n",
       "3    124.21  4.67  0.89  29.60  81.36  7.17  27.31  77.64  7.32  251.14   \n",
       "4    128.10  4.58  0.89  30.12  81.68  7.24  27.54  77.57  7.31  219.00   \n",
       "..      ...   ...   ...    ...    ...   ...    ...    ...   ...     ...   \n",
       "920  148.70  4.33  0.93  29.16  82.91  9.40  32.58  76.75  8.48  247.70   \n",
       "921  149.60  4.32  0.93  29.20  82.92  9.39  32.81  76.76  8.43  247.13   \n",
       "922  150.03  4.31  0.93  29.23  82.95  9.36  32.86  76.85  8.43  245.10   \n",
       "923  150.03  4.31  0.93  29.23  82.95  9.36  32.86  76.85  8.43  245.10   \n",
       "924  150.93  4.30  0.93  29.27  82.96  9.34  33.09  76.86  8.38  244.53   \n",
       "\n",
       "     scdlqd  sjhs  xscvm  xscvb  xi40  xi50  cu50  xsmj140  xsmj200  xshz  \n",
       "0      14.6  22.7  10.39    3.2   8.8   0.5   2.0     23.0      4.5  5.86  \n",
       "1      15.0  22.3  10.54    3.2   8.8   0.3   1.8     42.3      6.8  5.87  \n",
       "2      14.8  20.5  10.34    2.5   5.0   0.3   2.5     25.8      6.3  5.34  \n",
       "3      14.8  20.5  10.34    2.5   5.0   0.3   2.5     25.8      6.3  5.34  \n",
       "4      15.7  20.8  10.54    1.4   4.8   0.3   5.3     64.0     13.0  5.79  \n",
       "..      ...   ...    ...    ...   ...   ...   ...      ...      ...   ...  \n",
       "920    12.9  18.5  12.34    2.0  52.5   0.5  24.5    244.0     59.0  5.42  \n",
       "921    12.9  18.5  12.34    2.0  52.5   0.5  24.5    244.0     59.0  5.42  \n",
       "922    12.9  18.5  12.34    2.0  52.5   0.5  24.5    244.0     59.0  5.42  \n",
       "923    12.9  18.5  12.34    2.0  52.5   0.5  24.5    244.0     59.0  5.42  \n",
       "924    12.9  18.5  12.34    2.0  52.5   0.5  24.5    244.0     59.0  5.42  \n",
       "\n",
       "[925 rows x 20 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "miniature-withdrawal",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "blocked-latest",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     scdlqd  sjhs  xscvm  xscvb  xi40  xi50  cu50  xsmj140  xsmj200  xshz\n",
      "5      15.7  20.8  10.54    1.4   4.8   0.3   5.3     64.0     13.0  5.79\n",
      "6      15.5  20.5  10.70    2.2   9.8   0.3   6.3     57.5     13.0  5.98\n",
      "7      15.7  20.7  10.71    3.7   6.0   0.3   9.3     56.3     15.0  5.75\n",
      "8      15.7  20.6  10.93    2.8   5.3  11.7  13.3     75.6     15.1  5.88\n",
      "9      15.7  20.6  10.93    2.8   5.3  11.7  13.3     75.6     15.1  5.88\n",
      "..      ...   ...    ...    ...   ...   ...   ...      ...      ...   ...\n",
      "920    12.9  18.5  12.34    2.0  52.5   0.5  24.5    244.0     59.0  5.42\n",
      "921    12.9  18.5  12.34    2.0  52.5   0.5  24.5    244.0     59.0  5.42\n",
      "922    12.9  18.5  12.34    2.0  52.5   0.5  24.5    244.0     59.0  5.42\n",
      "923    12.9  18.5  12.34    2.0  52.5   0.5  24.5    244.0     59.0  5.42\n",
      "924    12.9  18.5  12.34    2.0  52.5   0.5  24.5    244.0     59.0  5.42\n",
      "\n",
      "[920 rows x 10 columns]        kfxs  mklz   csd   pjcd    zqd  dqzs  dlbqd    fsl    hd\n",
      "5    128.10  4.58  0.89  30.12  81.68  7.24  27.54  77.57  7.31\n",
      "6    125.63  4.80  0.89  29.57  81.81  7.13  27.62  77.39  7.50\n",
      "7    125.63  4.80  0.89  29.57  81.81  7.13  27.62  77.39  7.50\n",
      "8    127.32  4.80  0.90  29.38  81.47  7.24  28.85  77.73  7.41\n",
      "9    127.32  4.87  0.90  29.28  81.52  7.21  28.94  77.86  7.35\n",
      "..      ...   ...   ...    ...    ...   ...    ...    ...   ...\n",
      "920  148.70  4.33  0.93  29.16  82.91  9.40  32.58  76.75  8.48\n",
      "921  149.60  4.32  0.93  29.20  82.92  9.39  32.81  76.76  8.43\n",
      "922  150.03  4.31  0.93  29.23  82.95  9.36  32.86  76.85  8.43\n",
      "923  150.03  4.31  0.93  29.23  82.95  9.36  32.86  76.85  8.43\n",
      "924  150.93  4.30  0.93  29.27  82.96  9.34  33.09  76.86  8.38\n",
      "\n",
      "[920 rows x 9 columns]\n"
     ]
    }
   ],
   "source": [
    "# x输入 y输出要预测的列\n",
    "x = data.iloc[5:,10:]\n",
    "y = data.iloc[5:,:9]\n",
    "print(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "liberal-reach",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dense第一个参数（隐藏单元数，比如10个单元的隐含层），这个是自己设定可以是10也可以是100，参数越大，拟合能力越强，但是也会有缺点。\n",
    "# input_shape即张量的shape。输入多少个字段就设置几。 input_length：代表序列长度，可以理解成有多少个样本 input_dim：代表张量的维度。\n",
    "# 激活函数 （对中间层进行激活，提高拟合能力，正是神经元的工作原理 activation=  sigmoid/relu  ）\n",
    "# model = tf.keras.Sequential([tf.keras.layers.Dense(50,input_shape=(10,),activation='sigmoid'),\n",
    "#                              tf.keras.layers.Dense(50,input_shape=(10,),activation='sigmoid'),\n",
    "#                              tf.keras.layers.Dense(50,input_shape=(10,),activation='relu'),\n",
    "#                              tf.keras.layers.Dense(50,input_shape=(10,),activation='relu'),\n",
    "#                              tf.keras.layers.Dense(50,input_shape=(10,),activation='softmax'),\n",
    "#                              tf.keras.layers.Dense(50,input_shape=(10,),activation='sigmoid'),\n",
    "#                             tf.keras.layers.Dense(9)])\n",
    "\n",
    "model = tf.keras.Sequential()\n",
    "model.add(tf.keras.layers.Flatten(input_shape = (10,)))\n",
    "model.add(tf.keras.layers.Dense(88,activation=\"sigmoid\"))\n",
    "model.add(tf.keras.layers.Dense(88,activation=\"sigmoid\"))\n",
    "model.add(tf.keras.layers.Dropout(0.2))\n",
    "model.add(tf.keras.layers.Dense(88,activation=\"sigmoid\"))\n",
    "model.add(tf.keras.layers.Dense(88,activation=\"sigmoid\"))\n",
    "model.add(tf.keras.layers.Dense(9))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "average-kazakhstan",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "flatten (Flatten)            (None, 10)                0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 88)                968       \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 88)                7832      \n",
      "_________________________________________________________________\n",
      "dropout (Dropout)            (None, 88)                0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 88)                7832      \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 88)                7832      \n",
      "_________________________________________________________________\n",
      "dense_4 (Dense)              (None, 9)                 801       \n",
      "=================================================================\n",
      "Total params: 25,265\n",
      "Trainable params: 25,265\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# 中间层包含6个隐藏单元，一共有66个参数\n",
    "# 输入10个列 对应每一个单元需要十个单元，再加上一个偏置 所以有66个参数\n",
    "# x1*w1 x2*w2 x3*w3...再加一个biles  *6\n",
    "# 1*6+1偏置  7个参数\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "dynamic-payment",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 优化器optimizer：adam是最常用的 loss：输出是一个连续的值，均方差 mean square error\n",
    "    model.compile(optimizer =tf.keras.optimizers.Adam(learning_rate=0.001),loss='mse',metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "local-wallace",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/2500\n",
      "29/29 [==============================] - 1s 2ms/step - loss: 3491.6314 - accuracy: 0.2325\n",
      "Epoch 2/2500\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 3266.0270 - accuracy: 1.0000\n",
      "Epoch 3/2500\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 3086.2896 - accuracy: 1.0000\n",
      "Epoch 4/2500\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 2939.7217 - accuracy: 1.0000\n",
      "Epoch 5/2500\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 2816.0012 - accuracy: 1.0000\n",
      "Epoch 6/2500\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 2684.5630 - accuracy: 1.0000\n",
      "Epoch 7/2500\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 2555.3758 - accuracy: 1.0000\n",
      "Epoch 8/2500\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 2435.0422 - accuracy: 1.0000\n",
      "Epoch 9/2500\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 2315.4636 - accuracy: 1.0000\n",
      "Epoch 10/2500\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 2192.7227 - accuracy: 1.0000\n",
      "Epoch 11/2500\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 2085.6440 - accuracy: 1.0000\n",
      "Epoch 12/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 1994.0410 - accuracy: 1.0000\n",
      "Epoch 13/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 1887.2819 - accuracy: 1.0000\n",
      "Epoch 14/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 1804.2198 - accuracy: 1.0000\n",
      "Epoch 15/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 1700.2708 - accuracy: 1.0000\n",
      "Epoch 16/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 1632.4557 - accuracy: 1.0000\n",
      "Epoch 17/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 1548.0284 - accuracy: 1.0000\n",
      "Epoch 18/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 1463.0427 - accuracy: 1.0000\n",
      "Epoch 19/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 1404.9959 - accuracy: 1.0000\n",
      "Epoch 20/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 1339.0797 - accuracy: 1.0000\n",
      "Epoch 21/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 1278.2090 - accuracy: 1.0000\n",
      "Epoch 22/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 1216.7532 - accuracy: 1.0000\n",
      "Epoch 23/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 1154.0391 - accuracy: 1.0000\n",
      "Epoch 24/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 1097.1753 - accuracy: 1.0000\n",
      "Epoch 25/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 1046.0771 - accuracy: 1.0000\n",
      "Epoch 26/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 995.3944 - accuracy: 1.0000\n",
      "Epoch 27/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 943.9371 - accuracy: 1.0000\n",
      "Epoch 28/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 898.0193 - accuracy: 1.0000\n",
      "Epoch 29/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 853.1275 - accuracy: 1.0000\n",
      "Epoch 30/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 807.4287 - accuracy: 1.0000\n",
      "Epoch 31/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 763.1184 - accuracy: 1.0000\n",
      "Epoch 32/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 725.8137 - accuracy: 1.0000\n",
      "Epoch 33/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 690.8431 - accuracy: 1.0000\n",
      "Epoch 34/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 653.0117 - accuracy: 1.0000\n",
      "Epoch 35/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 622.9407 - accuracy: 1.0000\n",
      "Epoch 36/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 583.6617 - accuracy: 1.0000\n",
      "Epoch 37/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 557.4843 - accuracy: 1.0000\n",
      "Epoch 38/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 518.7106 - accuracy: 1.0000\n",
      "Epoch 39/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 486.4971 - accuracy: 1.0000\n",
      "Epoch 40/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 456.2036 - accuracy: 1.0000\n",
      "Epoch 41/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 431.1171 - accuracy: 1.0000\n",
      "Epoch 42/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 403.0345 - accuracy: 1.0000\n",
      "Epoch 43/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 379.4636 - accuracy: 1.0000\n",
      "Epoch 44/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 354.4029 - accuracy: 1.0000\n",
      "Epoch 45/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 334.5629 - accuracy: 1.0000\n",
      "Epoch 46/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 316.6304 - accuracy: 1.0000\n",
      "Epoch 47/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 292.0460 - accuracy: 1.0000\n",
      "Epoch 48/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 278.1951 - accuracy: 1.0000\n",
      "Epoch 49/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 260.2204 - accuracy: 1.0000\n",
      "Epoch 50/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 247.1226 - accuracy: 1.0000\n",
      "Epoch 51/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 232.0819 - accuracy: 1.0000\n",
      "Epoch 52/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 216.2131 - accuracy: 1.0000\n",
      "Epoch 53/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 204.3464 - accuracy: 1.0000\n",
      "Epoch 54/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 189.3788 - accuracy: 1.0000\n",
      "Epoch 55/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 176.1615 - accuracy: 1.0000\n",
      "Epoch 56/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 171.1366 - accuracy: 1.0000\n",
      "Epoch 57/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 153.9753 - accuracy: 1.0000\n",
      "Epoch 58/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 145.0632 - accuracy: 1.0000\n",
      "Epoch 59/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 139.0664 - accuracy: 1.0000\n",
      "Epoch 60/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 130.4371 - accuracy: 1.0000\n",
      "Epoch 61/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 121.3830 - accuracy: 1.0000\n",
      "Epoch 62/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 113.2763 - accuracy: 1.0000\n",
      "Epoch 63/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 108.4264 - accuracy: 1.0000\n",
      "Epoch 64/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 101.7912 - accuracy: 1.0000\n",
      "Epoch 65/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 92.1342 - accuracy: 1.0000\n",
      "Epoch 66/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 88.6022 - accuracy: 1.0000\n",
      "Epoch 67/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 82.9823 - accuracy: 1.0000\n",
      "Epoch 68/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 77.9831 - accuracy: 1.0000\n",
      "Epoch 69/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 73.2759 - accuracy: 1.0000\n",
      "Epoch 70/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 66.9682 - accuracy: 1.0000\n",
      "Epoch 71/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 63.0216 - accuracy: 1.0000\n",
      "Epoch 72/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 56.9735 - accuracy: 1.0000\n",
      "Epoch 73/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 56.5919 - accuracy: 1.0000\n",
      "Epoch 74/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 49.7669 - accuracy: 1.0000\n",
      "Epoch 75/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 48.7434 - accuracy: 1.0000\n",
      "Epoch 76/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 46.2404 - accuracy: 1.0000\n",
      "Epoch 77/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 43.6394 - accuracy: 1.0000\n",
      "Epoch 78/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 39.5964 - accuracy: 1.0000\n",
      "Epoch 79/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 37.2881 - accuracy: 1.0000\n",
      "Epoch 80/2500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "29/29 [==============================] - 0s 1ms/step - loss: 35.4265 - accuracy: 1.0000\n",
      "Epoch 81/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 32.6688 - accuracy: 1.0000\n",
      "Epoch 82/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 30.0398 - accuracy: 1.0000\n",
      "Epoch 83/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 29.8615 - accuracy: 1.0000\n",
      "Epoch 84/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 27.0689 - accuracy: 1.0000\n",
      "Epoch 85/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 27.1768 - accuracy: 1.0000\n",
      "Epoch 86/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 25.3473 - accuracy: 1.0000\n",
      "Epoch 87/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 22.7763 - accuracy: 1.0000\n",
      "Epoch 88/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 21.2201 - accuracy: 1.0000\n",
      "Epoch 89/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 19.9503 - accuracy: 1.0000\n",
      "Epoch 90/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 19.6164 - accuracy: 1.0000\n",
      "Epoch 91/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 17.7526 - accuracy: 1.0000\n",
      "Epoch 92/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 17.9139 - accuracy: 1.0000\n",
      "Epoch 93/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 15.7215 - accuracy: 1.0000\n",
      "Epoch 94/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 15.1129 - accuracy: 1.0000\n",
      "Epoch 95/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 14.7650 - accuracy: 1.0000\n",
      "Epoch 96/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 15.0040 - accuracy: 1.0000\n",
      "Epoch 97/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 13.2145 - accuracy: 1.0000\n",
      "Epoch 98/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 13.0994 - accuracy: 1.0000\n",
      "Epoch 99/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 12.8716 - accuracy: 1.0000\n",
      "Epoch 100/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 12.3270 - accuracy: 1.0000\n",
      "Epoch 101/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 11.0099 - accuracy: 1.0000\n",
      "Epoch 102/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 12.0728 - accuracy: 1.0000\n",
      "Epoch 103/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 11.4588 - accuracy: 1.0000\n",
      "Epoch 104/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 11.3985 - accuracy: 1.0000\n",
      "Epoch 105/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 10.5858 - accuracy: 1.0000\n",
      "Epoch 106/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 10.9712 - accuracy: 1.0000\n",
      "Epoch 107/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 10.1017 - accuracy: 1.0000\n",
      "Epoch 108/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 10.8082 - accuracy: 1.0000\n",
      "Epoch 109/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 10.3433 - accuracy: 1.0000\n",
      "Epoch 110/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 9.9045 - accuracy: 1.0000\n",
      "Epoch 111/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 9.2475 - accuracy: 1.0000\n",
      "Epoch 112/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 9.3801 - accuracy: 1.0000\n",
      "Epoch 113/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 8.8752 - accuracy: 1.0000\n",
      "Epoch 114/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 9.0508 - accuracy: 1.0000\n",
      "Epoch 115/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 9.3350 - accuracy: 1.0000\n",
      "Epoch 116/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 9.2799 - accuracy: 1.0000\n",
      "Epoch 117/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 9.4629 - accuracy: 1.0000\n",
      "Epoch 118/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 9.3517 - accuracy: 1.0000\n",
      "Epoch 119/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 8.7788 - accuracy: 1.0000\n",
      "Epoch 120/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 8.6356 - accuracy: 1.0000\n",
      "Epoch 121/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 8.5415 - accuracy: 1.0000\n",
      "Epoch 122/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 9.1466 - accuracy: 1.0000\n",
      "Epoch 123/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 9.8272 - accuracy: 1.0000\n",
      "Epoch 124/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 8.8375 - accuracy: 1.0000\n",
      "Epoch 125/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 8.9279 - accuracy: 1.0000\n",
      "Epoch 126/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 9.0047 - accuracy: 1.0000\n",
      "Epoch 127/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 8.8190 - accuracy: 1.0000\n",
      "Epoch 128/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 8.5687 - accuracy: 1.0000\n",
      "Epoch 129/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 8.9168 - accuracy: 1.0000\n",
      "Epoch 130/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 8.5620 - accuracy: 1.0000\n",
      "Epoch 131/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 8.8690 - accuracy: 1.0000\n",
      "Epoch 132/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 8.7187 - accuracy: 1.0000\n",
      "Epoch 133/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 8.3170 - accuracy: 1.0000\n",
      "Epoch 134/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 9.0756 - accuracy: 1.0000\n",
      "Epoch 135/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 9.5968 - accuracy: 1.0000\n",
      "Epoch 136/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 9.0789 - accuracy: 1.0000\n",
      "Epoch 137/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 9.0615 - accuracy: 1.0000\n",
      "Epoch 138/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 8.2972 - accuracy: 1.0000\n",
      "Epoch 139/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 8.8752 - accuracy: 1.0000\n",
      "Epoch 140/2500\n",
      "29/29 [==============================] - 0s 997us/step - loss: 8.8167 - accuracy: 1.0000\n",
      "Epoch 141/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 8.8292 - accuracy: 1.0000\n",
      "Epoch 142/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 9.2279 - accuracy: 1.0000\n",
      "Epoch 143/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 8.9806 - accuracy: 1.0000\n",
      "Epoch 144/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 9.1332 - accuracy: 1.0000\n",
      "Epoch 145/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 9.1248 - accuracy: 1.0000\n",
      "Epoch 146/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 8.2607 - accuracy: 1.0000\n",
      "Epoch 147/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 8.7669 - accuracy: 1.0000\n",
      "Epoch 148/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 8.4913 - accuracy: 1.0000\n",
      "Epoch 149/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 8.8551 - accuracy: 1.0000\n",
      "Epoch 150/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 8.4536 - accuracy: 1.0000\n",
      "Epoch 151/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 9.1499 - accuracy: 1.0000\n",
      "Epoch 152/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 8.9345 - accuracy: 1.0000\n",
      "Epoch 153/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 8.3787 - accuracy: 1.0000\n",
      "Epoch 154/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 9.0720 - accuracy: 1.0000\n",
      "Epoch 155/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 8.6233 - accuracy: 1.0000\n",
      "Epoch 156/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 8.9562 - accuracy: 1.0000\n",
      "Epoch 157/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 8.8335 - accuracy: 1.0000\n",
      "Epoch 158/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 9.0559 - accuracy: 1.0000\n",
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     ]
    },
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     ]
    },
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    },
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     ]
    },
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     ]
    },
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    },
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     ]
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      "Epoch 1988/2500\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "29/29 [==============================] - 0s 1ms/step - loss: 0.2973 - accuracy: 1.0000\n",
      "Epoch 1989/2500\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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      "29/29 [==============================] - 0s 1ms/step - loss: 0.6974 - accuracy: 1.0000\n",
      "Epoch 2447/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 0.5830 - accuracy: 1.0000\n",
      "Epoch 2448/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 0.5121 - accuracy: 1.0000\n",
      "Epoch 2449/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 0.3992 - accuracy: 1.0000\n",
      "Epoch 2450/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 0.3323 - accuracy: 1.0000\n",
      "Epoch 2451/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 0.2868 - accuracy: 1.0000\n",
      "Epoch 2452/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 0.2621 - accuracy: 1.0000\n",
      "Epoch 2453/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 0.2820 - accuracy: 1.0000\n",
      "Epoch 2454/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 0.2679 - accuracy: 1.0000\n",
      "Epoch 2455/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 0.2480 - accuracy: 1.0000\n",
      "Epoch 2456/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 0.2555 - accuracy: 1.0000\n",
      "Epoch 2457/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 0.2422 - accuracy: 1.0000\n",
      "Epoch 2458/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 0.2523 - accuracy: 1.0000\n",
      "Epoch 2459/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 0.2653 - accuracy: 1.0000\n",
      "Epoch 2460/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 0.2380 - accuracy: 1.0000\n",
      "Epoch 2461/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 0.2706 - accuracy: 1.0000\n",
      "Epoch 2462/2500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "29/29 [==============================] - 0s 1ms/step - loss: 0.2756 - accuracy: 1.0000\n",
      "Epoch 2463/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 0.2624 - accuracy: 1.0000\n",
      "Epoch 2464/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 0.2468 - accuracy: 1.0000\n",
      "Epoch 2465/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 0.2355 - accuracy: 1.0000\n",
      "Epoch 2466/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 0.2475 - accuracy: 1.0000\n",
      "Epoch 2467/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 0.2511 - accuracy: 1.0000\n",
      "Epoch 2468/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 0.2725 - accuracy: 1.0000\n",
      "Epoch 2469/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 0.2823 - accuracy: 1.0000\n",
      "Epoch 2470/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 0.2555 - accuracy: 1.0000\n",
      "Epoch 2471/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 0.2433 - accuracy: 1.0000\n",
      "Epoch 2472/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 0.2623 - accuracy: 1.0000\n",
      "Epoch 2473/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 0.2769 - accuracy: 1.0000\n",
      "Epoch 2474/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 0.2766 - accuracy: 1.0000\n",
      "Epoch 2475/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 0.2699 - accuracy: 1.0000\n",
      "Epoch 2476/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 0.2616 - accuracy: 1.0000\n",
      "Epoch 2477/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 0.2553 - accuracy: 1.0000\n",
      "Epoch 2478/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 0.2461 - accuracy: 1.0000\n",
      "Epoch 2479/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 0.2736 - accuracy: 1.0000\n",
      "Epoch 2480/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 0.2694 - accuracy: 1.0000\n",
      "Epoch 2481/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 0.2502 - accuracy: 1.0000\n",
      "Epoch 2482/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 0.2462 - accuracy: 1.0000\n",
      "Epoch 2483/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 0.2228 - accuracy: 1.0000\n",
      "Epoch 2484/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 0.2370 - accuracy: 1.0000\n",
      "Epoch 2485/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 0.2573 - accuracy: 1.0000\n",
      "Epoch 2486/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 0.2769 - accuracy: 1.0000\n",
      "Epoch 2487/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 0.2963 - accuracy: 1.0000\n",
      "Epoch 2488/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 0.2737 - accuracy: 1.0000\n",
      "Epoch 2489/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 0.2756 - accuracy: 1.0000\n",
      "Epoch 2490/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 0.2274 - accuracy: 1.0000\n",
      "Epoch 2491/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 0.2334 - accuracy: 1.0000\n",
      "Epoch 2492/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 0.2613 - accuracy: 1.0000\n",
      "Epoch 2493/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 0.2781 - accuracy: 1.0000\n",
      "Epoch 2494/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 0.2361 - accuracy: 1.0000\n",
      "Epoch 2495/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 0.2319 - accuracy: 1.0000\n",
      "Epoch 2496/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 0.3197 - accuracy: 1.0000\n",
      "Epoch 2497/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 0.2694 - accuracy: 1.0000\n",
      "Epoch 2498/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 0.2700 - accuracy: 1.0000\n",
      "Epoch 2499/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 0.2790 - accuracy: 1.0000\n",
      "Epoch 2500/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 0.2259 - accuracy: 1.0000\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x7ff84c487978>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# epochs 训练多少次\n",
    "model.fit(x,y,epochs = 2500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "cross-generator",
   "metadata": {},
   "outputs": [],
   "source": [
    "# [预测哪些值，输入哪些列]\n",
    "test = data.iloc[0:50,10:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "wound-cardiff",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[128.13414   ,   4.695614  ,   0.9188588 ,  28.57126   ,\n",
       "         82.60368   ,   7.168845  ,  29.53151   ,  69.709564  ,\n",
       "          8.792501  ],\n",
       "       [130.3685    ,   3.8153949 ,   0.85359764,  28.034029  ,\n",
       "         82.925316  ,   6.7337027 ,  26.790491  ,  72.39029   ,\n",
       "          7.939356  ],\n",
       "       [128.55269   ,   4.6489363 ,   0.9148464 ,  28.584328  ,\n",
       "         82.627304  ,   7.0690894 ,  29.45637   ,  69.50108   ,\n",
       "          8.721316  ],\n",
       "       [128.55269   ,   4.6489363 ,   0.9148464 ,  28.584328  ,\n",
       "         82.627304  ,   7.0690894 ,  29.45637   ,  69.50108   ,\n",
       "          8.721316  ],\n",
       "       [126.91876   ,   4.78287   ,   0.8972388 ,  29.644884  ,\n",
       "         81.79658   ,   7.1394424 ,  27.98208   ,  78.02405   ,\n",
       "          7.4727983 ],\n",
       "       [126.91876   ,   4.78287   ,   0.8972388 ,  29.644884  ,\n",
       "         81.79658   ,   7.1394424 ,  27.98208   ,  78.02405   ,\n",
       "          7.4727983 ],\n",
       "       [126.77172   ,   4.7811813 ,   0.8967243 ,  29.634714  ,\n",
       "         81.7827    ,   7.1342144 ,  27.947653  ,  78.01511   ,\n",
       "          7.4823227 ],\n",
       "       [126.07746   ,   4.7439203 ,   0.8977712 ,  29.611553  ,\n",
       "         81.87869   ,   6.9987717 ,  27.509317  ,  77.26041   ,\n",
       "          7.5721054 ],\n",
       "       [127.12119   ,   4.7786536 ,   0.8948468 ,  29.648151  ,\n",
       "         81.77413   ,   7.165124  ,  28.039164  ,  77.97337   ,\n",
       "          7.4663124 ],\n",
       "       [127.12119   ,   4.7786536 ,   0.8948468 ,  29.648151  ,\n",
       "         81.77413   ,   7.165124  ,  28.039164  ,  77.97337   ,\n",
       "          7.4663124 ],\n",
       "       [126.65933   ,   4.770444  ,   0.8969069 ,  29.62812   ,\n",
       "         81.7591    ,   7.123874  ,  27.929352  ,  77.947296  ,\n",
       "          7.4715414 ],\n",
       "       [126.65933   ,   4.770444  ,   0.8969069 ,  29.62812   ,\n",
       "         81.7591    ,   7.123874  ,  27.929352  ,  77.947296  ,\n",
       "          7.4715414 ],\n",
       "       [126.7884    ,   4.7783866 ,   0.8984747 ,  29.640303  ,\n",
       "         81.78739   ,   7.1295376 ,  27.936941  ,  77.96356   ,\n",
       "          7.462901  ],\n",
       "       [126.7884    ,   4.7783866 ,   0.8984747 ,  29.640303  ,\n",
       "         81.78739   ,   7.1295376 ,  27.936941  ,  77.96356   ,\n",
       "          7.462901  ],\n",
       "       [126.836105  ,   4.781232  ,   0.89660054,  29.642813  ,\n",
       "         81.78655   ,   7.1376143 ,  27.96468   ,  78.01526   ,\n",
       "          7.4711924 ],\n",
       "       [126.836105  ,   4.781232  ,   0.89660054,  29.642813  ,\n",
       "         81.78655   ,   7.1376143 ,  27.96468   ,  78.01526   ,\n",
       "          7.4711924 ],\n",
       "       [127.0575    ,   4.7883573 ,   0.89505726,  29.660246  ,\n",
       "         81.784645  ,   7.1592298 ,  28.027472  ,  78.00145   ,\n",
       "          7.4614534 ],\n",
       "       [127.0575    ,   4.7883573 ,   0.89505726,  29.660246  ,\n",
       "         81.784645  ,   7.1592298 ,  28.027472  ,  78.00145   ,\n",
       "          7.4614534 ],\n",
       "       [122.4194    ,   4.728765  ,   0.8918482 ,  29.210608  ,\n",
       "         81.70882   ,   7.1419477 ,  26.662956  ,  77.58498   ,\n",
       "          7.5805855 ],\n",
       "       [122.4194    ,   4.728765  ,   0.8918482 ,  29.210608  ,\n",
       "         81.70882   ,   7.1419477 ,  26.662956  ,  77.58498   ,\n",
       "          7.5805855 ],\n",
       "       [126.390015  ,   4.759558  ,   0.89873093,  29.59461   ,\n",
       "         81.77188   ,   7.080153  ,  27.837587  ,  78.02098   ,\n",
       "          7.5016756 ],\n",
       "       [126.390015  ,   4.759558  ,   0.89873093,  29.59461   ,\n",
       "         81.77188   ,   7.080153  ,  27.837587  ,  78.02098   ,\n",
       "          7.5016756 ],\n",
       "       [125.67562   ,   4.7347927 ,   0.9009758 ,  29.57249   ,\n",
       "         81.84197   ,   6.951425  ,  27.3074    ,  76.85336   ,\n",
       "          7.59111   ],\n",
       "       [125.67562   ,   4.7347927 ,   0.9009758 ,  29.57249   ,\n",
       "         81.84197   ,   6.951425  ,  27.3074    ,  76.85336   ,\n",
       "          7.59111   ],\n",
       "       [125.722115  ,   4.7412314 ,   0.9032292 ,  29.56585   ,\n",
       "         81.81165   ,   6.9635963 ,  27.35231   ,  76.92584   ,\n",
       "          7.5686903 ],\n",
       "       [125.722115  ,   4.7412314 ,   0.9032292 ,  29.56585   ,\n",
       "         81.81165   ,   6.9635963 ,  27.35231   ,  76.92584   ,\n",
       "          7.5686903 ],\n",
       "       [126.94933   ,   4.787422  ,   0.8978565 ,  29.641058  ,\n",
       "         81.75919   ,   7.149649  ,  27.993923  ,  77.96149   ,\n",
       "          7.4471207 ],\n",
       "       [126.94933   ,   4.787422  ,   0.8978565 ,  29.641058  ,\n",
       "         81.75919   ,   7.149649  ,  27.993923  ,  77.96149   ,\n",
       "          7.4471207 ],\n",
       "       [126.94933   ,   4.787422  ,   0.8978565 ,  29.641058  ,\n",
       "         81.75919   ,   7.149649  ,  27.993923  ,  77.96149   ,\n",
       "          7.4471207 ],\n",
       "       [126.94933   ,   4.787422  ,   0.8978565 ,  29.641058  ,\n",
       "         81.75919   ,   7.149649  ,  27.993923  ,  77.96149   ,\n",
       "          7.4471207 ],\n",
       "       [126.82833   ,   4.781371  ,   0.8952991 ,  29.649426  ,\n",
       "         81.79089   ,   7.1386456 ,  27.94586   ,  77.9129    ,\n",
       "          7.465186  ],\n",
       "       [126.82833   ,   4.781371  ,   0.8952991 ,  29.649426  ,\n",
       "         81.79089   ,   7.1386456 ,  27.94586   ,  77.9129    ,\n",
       "          7.465186  ],\n",
       "       [126.82833   ,   4.781371  ,   0.8952991 ,  29.649426  ,\n",
       "         81.79089   ,   7.1386456 ,  27.94586   ,  77.9129    ,\n",
       "          7.465186  ],\n",
       "       [126.82833   ,   4.781371  ,   0.8952991 ,  29.649426  ,\n",
       "         81.79089   ,   7.1386456 ,  27.94586   ,  77.9129    ,\n",
       "          7.465186  ],\n",
       "       [126.53586   ,   4.761707  ,   0.8984711 ,  29.629732  ,\n",
       "         81.853455  ,   7.0658894 ,  27.759388  ,  77.697205  ,\n",
       "          7.5224705 ],\n",
       "       [126.53586   ,   4.761707  ,   0.8984711 ,  29.629732  ,\n",
       "         81.853455  ,   7.0658894 ,  27.759388  ,  77.697205  ,\n",
       "          7.5224705 ],\n",
       "       [126.53586   ,   4.761707  ,   0.8984711 ,  29.629732  ,\n",
       "         81.853455  ,   7.0658894 ,  27.759388  ,  77.697205  ,\n",
       "          7.5224705 ],\n",
       "       [126.78548   ,   4.780434  ,   0.898262  ,  29.645288  ,\n",
       "         81.806564  ,   7.1250234 ,  27.93619   ,  78.001236  ,\n",
       "          7.467738  ],\n",
       "       [126.78548   ,   4.780434  ,   0.898262  ,  29.645288  ,\n",
       "         81.806564  ,   7.1250234 ,  27.93619   ,  78.001236  ,\n",
       "          7.467738  ],\n",
       "       [126.78548   ,   4.780434  ,   0.898262  ,  29.645288  ,\n",
       "         81.806564  ,   7.1250234 ,  27.93619   ,  78.001236  ,\n",
       "          7.467738  ],\n",
       "       [126.64245   ,   4.783252  ,   0.8972432 ,  29.621185  ,\n",
       "         81.777405  ,   7.124562  ,  27.909893  ,  78.015854  ,\n",
       "          7.497077  ],\n",
       "       [126.64245   ,   4.783252  ,   0.8972432 ,  29.621185  ,\n",
       "         81.777405  ,   7.124562  ,  27.909893  ,  78.015854  ,\n",
       "          7.497077  ],\n",
       "       [126.64245   ,   4.783252  ,   0.8972432 ,  29.621185  ,\n",
       "         81.777405  ,   7.124562  ,  27.909893  ,  78.015854  ,\n",
       "          7.497077  ],\n",
       "       [126.90034   ,   4.7825055 ,   0.8967757 ,  29.648163  ,\n",
       "         81.79963   ,   7.1402044 ,  27.96666   ,  77.9919    ,\n",
       "          7.4669666 ],\n",
       "       [126.90034   ,   4.7825055 ,   0.8967757 ,  29.648163  ,\n",
       "         81.79963   ,   7.1402044 ,  27.96666   ,  77.9919    ,\n",
       "          7.4669666 ],\n",
       "       [126.90034   ,   4.7825055 ,   0.8967757 ,  29.648163  ,\n",
       "         81.79963   ,   7.1402044 ,  27.96666   ,  77.9919    ,\n",
       "          7.4669666 ],\n",
       "       [126.90034   ,   4.7825055 ,   0.8967757 ,  29.648163  ,\n",
       "         81.79963   ,   7.1402044 ,  27.96666   ,  77.9919    ,\n",
       "          7.4669666 ],\n",
       "       [126.839905  ,   4.7891626 ,   0.89616525,  29.650286  ,\n",
       "         81.78645   ,   7.1510563 ,  27.97379   ,  77.98825   ,\n",
       "          7.4647875 ],\n",
       "       [126.839905  ,   4.7891626 ,   0.89616525,  29.650286  ,\n",
       "         81.78645   ,   7.1510563 ,  27.97379   ,  77.98825   ,\n",
       "          7.4647875 ],\n",
       "       [126.839905  ,   4.7891626 ,   0.89616525,  29.650286  ,\n",
       "         81.78645   ,   7.1510563 ,  27.97379   ,  77.98825   ,\n",
       "          7.4647875 ]], dtype=float32)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "capital-teacher",
   "metadata": {},
   "outputs": [],
   "source": [
    "true_data = data.iloc[0:50,:9]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "geological-series",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      kfxs  mklz   csd   pjcd    zqd  dqzs  dlbqd    fsl    hd\n",
      "0   126.43  4.84  0.90  29.04  81.89  7.14  28.28  77.14  7.44\n",
      "1   126.43  4.84  0.90  29.04  81.89  7.14  28.28  77.14  7.44\n",
      "2   125.64  4.64  0.89  29.63  81.60  7.13  27.28  77.85  7.34\n",
      "3   124.21  4.67  0.89  29.60  81.36  7.17  27.31  77.64  7.32\n",
      "4   128.10  4.58  0.89  30.12  81.68  7.24  27.54  77.57  7.31\n",
      "5   128.10  4.58  0.89  30.12  81.68  7.24  27.54  77.57  7.31\n",
      "6   125.63  4.80  0.89  29.57  81.81  7.13  27.62  77.39  7.50\n",
      "7   125.63  4.80  0.89  29.57  81.81  7.13  27.62  77.39  7.50\n",
      "8   127.32  4.80  0.90  29.38  81.47  7.24  28.85  77.73  7.41\n",
      "9   127.32  4.87  0.90  29.28  81.52  7.21  28.94  77.86  7.35\n",
      "10  127.32  4.80  0.90  29.38  81.47  7.24  28.85  77.73  7.41\n",
      "11  127.32  4.87  0.90  29.28  81.52  7.21  28.94  77.86  7.35\n",
      "12  127.32  4.80  0.90  29.38  81.47  7.24  28.85  77.73  7.41\n",
      "13  127.32  4.87  0.90  29.28  81.52  7.21  28.94  77.86  7.35\n",
      "14  127.32  4.80  0.90  29.38  81.47  7.24  28.85  77.73  7.41\n",
      "15  127.32  4.87  0.90  29.28  81.52  7.21  28.94  77.86  7.35\n",
      "16  127.32  4.80  0.90  29.38  81.47  7.24  28.85  77.73  7.41\n",
      "17  127.32  4.87  0.90  29.28  81.52  7.21  28.94  77.86  7.35\n",
      "18  122.13  4.72  0.88  29.47  81.65  7.03  26.37  77.83  7.44\n",
      "19  122.13  4.71  0.88  29.45  81.64  7.04  26.37  77.83  7.45\n",
      "20  125.00  4.56  0.88  29.61  82.02  6.94  26.66  78.16  7.34\n",
      "21  125.60  4.55  0.88  29.55  82.09  6.91  26.70  78.22  7.34\n",
      "22  125.00  4.56  0.88  29.61  82.02  6.94  26.66  78.16  7.34\n",
      "23  125.60  4.55  0.88  29.55  82.09  6.91  26.70  78.22  7.34\n",
      "24  125.00  4.56  0.88  29.61  82.02  6.94  26.66  78.16  7.34\n",
      "25  125.60  4.55  0.88  29.55  82.09  6.91  26.70  78.22  7.34\n",
      "26  126.33  4.79  0.89  29.61  81.84  7.07  27.47  78.22  7.53\n",
      "27  126.44  4.79  0.89  29.62  81.86  7.07  27.47  78.30  7.50\n",
      "28  126.79  4.76  0.89  29.63  81.88  7.05  27.43  78.34  7.53\n",
      "29  127.13  4.73  0.89  29.64  81.68  7.13  27.79  78.30  7.56\n",
      "30  126.33  4.79  0.89  29.61  81.84  7.07  27.47  78.22  7.53\n",
      "31  126.44  4.79  0.89  29.62  81.86  7.07  27.47  78.30  7.50\n",
      "32  126.79  4.76  0.89  29.63  81.88  7.05  27.43  78.34  7.53\n",
      "33  127.13  4.73  0.89  29.64  81.68  7.13  27.79  78.30  7.56\n",
      "34  127.09  4.84  0.90  29.72  82.00  6.98  27.96  77.72  7.64\n",
      "35  127.09  4.84  0.90  29.72  82.00  6.98  27.96  77.72  7.64\n",
      "36  126.16  4.86  0.90  29.59  82.02  7.01  27.71  77.76  7.62\n",
      "37  127.09  4.84  0.90  29.72  82.00  6.98  27.96  77.72  7.64\n",
      "38  127.09  4.84  0.90  29.72  82.00  6.98  27.96  77.72  7.64\n",
      "39  126.16  4.86  0.90  29.59  82.02  7.01  27.71  77.76  7.62\n",
      "40  127.09  4.84  0.90  29.72  82.00  6.98  27.96  77.72  7.64\n",
      "41  127.09  4.84  0.90  29.72  82.00  6.98  27.96  77.72  7.64\n",
      "42  126.16  4.86  0.90  29.59  82.02  7.01  27.71  77.76  7.62\n",
      "43  126.33  4.79  0.89  29.61  81.84  7.07  27.47  78.22  7.53\n",
      "44  126.44  4.79  0.89  29.62  81.86  7.07  27.47  78.30  7.50\n",
      "45  126.79  4.76  0.89  29.63  81.88  7.05  27.43  78.34  7.53\n",
      "46  127.13  4.73  0.89  29.64  81.68  7.13  27.79  78.30  7.56\n",
      "47  127.09  4.84  0.90  29.72  82.00  6.98  27.96  77.72  7.64\n",
      "48  127.09  4.84  0.90  29.72  82.00  6.98  27.96  77.72  7.64\n",
      "49  126.16  4.86  0.90  29.59  82.02  7.01  27.71  77.76  7.62\n"
     ]
    }
   ],
   "source": [
    "print(true_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "closing-keyboard",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "regulation-ordering",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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